How do I use audit and inspection data to improve workplace safety?

Q:

Our company collects data nearly nonstop. Organizing, understanding, and using it in our decision-making process can be difficult. What’s the best way to put our safety data to work?

A:

There are three stages of data implementation: short-term, mid-term, and long-term. As you might imagine, short-term implementation involves the initial collection and reaction to data. Mid-term includes next steps, follow-up tasks, and action plans. Long-term is focused on the “big picture” of longitudinal trends.

Short-Term Data

Putting the “action” in “actionable data” starts with the collection process. When an inspector first documents an issue, the initial response is crucial. If they’re performing an inspection on paper or aggregating it on spreadsheets, the ensuing emails chains or conference calls can stymie responsiveness. Ideally, your organization will have EHS software in place that provides on-the-spot feedback for corrections, instant alerts, and automatically generated task assignments.

This critical first step doesn’t just determine how much time, effort, and revenue is lost when machinery breaks down or an injury halts production; it also affects how that information is understood over time. Effective reporting requires thorough documentation, including supporting evidence like images, notes, and remediation logs. Most importantly, you must communicate issues and their ownership quickly, clearly, and definitively to ensure that short-term data is used to its full potential.

Mid-Term Data

The goal of initial data collection is to ensure that whoever handles the information next understands everything they need to know at face value. For data to migrate successfully from short- to mid-term, your organization must establish realistic priorities, intuitive task trees, and manageable workloads. Once you have aligned your organization’s data management processes and everyone has “bought in” to your solution, it’s just a matter of ensuring best practices are followed.

Consider how your data pipeline filters ongoing tasks and issues, how criticality levels are determined, and on what basis the responsible parties are assigned workflows. The answers to those questions should give you a broad picture of your mid-stream data implementation in its current state. Like many companies, mid-stream may be where your data begins to break down, either due to its eventual archival in “silos,” a lack of proper integration, or a failure to aggregate it in one system.

Almost everything involved in mid-term data utilization can be automated using EHS inspection software with task management and workflow tools. On the other hand, without a reliable system for automating follow-up tasks and data filtration, it’s easy to lose traceability. Root cause analysis and long-term analytics become increasingly difficult to visualize and communicate without consistent, organized metrics.

Long-Term Data

Applying long-term data doesn’t mean you’re directly reducing the causes linked to safety issues. Instead, it’s about incremental changes over time, or continuous improvement. More often than not, the conditions that lead to catastrophic incidents are not the same as the variables that cause minor accidents and near misses.

As BP wrote in their report on the Deepwater Horizon incident, no single cause triggered the fatal event: “Rather, a complex and interlinked series of mechanical failures, human judgments, engineering design, operational implementation, and team interfaces came together to allow the initiation and escalation of the accident.” To put it bluntly, interpreting reports as the culmination of various, nuanced issues requires more than a fifteen-minute session of “5 Whys.” Changes have to be granular, achievable, and measurable (learn more in Lessons from 3 of the Worst Workplace Disasters).

So what does that mean for the power of longitudinal data analytics? First, the bad: any tech firm that claims to have a magic bullet to quell all your safety concerns is selling you snake oil. While the use of predictive analytics to root out issues before they happen shows positive results in machine learning and closed-loop use cases, they do little to account for unpredictable human behavior and faulty logic.

The good news is that your data analysis tools can help you create a testing ground for incremental improvements. The right EHS software will not only provide actionable awareness of where processes are breaking down but also paint a clear picture of the gradual steps needed to resolve issues.

Tying the Data Together

One of the best ways to measure how responsive your reporting and analysis tools are is by experimenting with their scope: How wide and how narrow a lens can you use to filter your data? Do your tools allows you to examine changes over time by region and location? What about comparisons between different field sites, work teams, processes, or individual employees?

The end goal of data utilization is simple: collect reliable information, then use it to keep workers, assets, and the bottom line safe. Consider whether your current solution gives you the ability to tie short-, mid-, and long-term data into real, measurable improvements. If you’re struggling to connect the dots from initial reporting to long-term initiatives, it might be time to upgrade to a mobile platform for EHS inspections.

Alexander Zagvazdin is a veteran technologist and industry thought leader with a focus on mobile innovation and product development. Currently serving as the VP of Product at WorldAPP, Alexander holds a PhD from Glushkov Institute of Cybernetics in Speech Signal Processing. His previous roles in his 12-year tenure at WorldAPP include serving as the company’s Product Manager, Director of Technology, and CTO.
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